Bridge Monitoring Signal Noise Reduction Method for EMD Joint Improvement of Wavelet Threshold

Yi Zhang

doi:10.54691/z25be690

Authors

Yi Zhang

DOI:

https://doi.org/10.54691/z25be690

Keywords:

Bridge Monitoring Signal; Set Empirical Mode Decomposition; Noise Reduction at the Wavelet Threshold; Analog Signal.

Abstract

When the bridge conducts status assessment and health monitoring, the obtained bridge signals are susceptible to the interference of the external environment and are difficult to reflect the true response of the bridge structure. Based on the bridge monitoring signal noise reduction method for EEMD to improve the wavelet threshold. This method first uses EEMD to perform adaptive decomposition of the signal containing noise, then removes the modes with small variance contribution rate, and finally performs the wavelet threshold denoising of the remaining modes to reconstruct the real signal after denoising. On the simulated signals, the results show that the noise reduction method of EEMD can filter the interference noise signal effectively, and the noise effect is better than the single improvement, EMD and EMD denoising, and the research results can provide meaningful reference for the bridge signal denoising processing.

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References

[1] Huang N E, Shen Z, Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for no nlinear and non-stationary time series analysis, Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, vol.454(1998),p. 903-995.

[2] Boudraa,Abdel-Ouahab,and Jean-Christophe Cexus. "EMD-based signal filtering."IEEE transactions on instrumentation and measurement" ,Vol.56(2007)No.6,p. 2196-2202.

[3] Du Xiuli, He Lizhi, Hou Wei. The wavelet threshold noise method based on empirical mode decomposition (EMD) , Journal of Beijing University of Technology, Vol 03(2007),p. 265-272.

[4] Wang Binbin. Application of the Kalman filter algorithm in noise reduction by sedimentation analysis of tunnel structures, Journal of Beijing University of Civil Engineering and Architecture, Vol.37(2021)No.03,p. 64-69.

[5] JANG Yuan, SHANG Guan-Bia0, ZENG Jing-kai. Application of improved waveletthreshold algorithm based on EEMD in ultrasonic water meter , Journal of Vibration andShock, Vol.41(2022)No.05,p. 208-213.

[6] WANG Hai-bo, YE Ru-shan, DU Wu. Bridge vibration based on EMD and wavelet threshold mixed signal denoising method , Highway, Vol.66(2021)No.12,p.110-116.